A Neural Network-based Semiconductor Price Prediction System

Abstract

The purpose of this study is to support the decision of semiconductor companies by providing an objective chip price prediction model. Existing statistical or econometric models have shown limits analyzing nonlinear time-series data such as share prices and exchange rates. The back-propagation algorithm, which is the most common method, was used as the learning algorithm. Predicting was attempted by using two supply factor variables and four demand factor variables. The data used in the analysis was collected from January 3, 2003 to December 28, 2005. The data has been divided into two parts for learning and verification. As a result of inputting the verification data into the trained neural network, the actual values show some differences. However, we were able to see that the flow of the semiconductor market and short-term forecasting was possible providing very little error between the predicted value and the actual value.